Driving Your AI/ML Plans
Everyone knows artificial intelligence and machine learning algorithms (AI/ML) need data to be valuable, but many organizations forget they have enormous data sets from their test programs – mainly because it is inaccessible due to poor organization, management, and curation. Proper Test Data Management (TDM) can be hard to setup and maintain but is worth the effort to fuel AI/ML. Gains can be made using these empowered algorithms in test efficiency, effectiveness, and overall business operations. You want to avoid getting lost in a sea of data, running into bottle necks, or leaving key requirements unmet as you climb the right side of the systems engineering V.
In the Age of Big Data and AI, Test Data Emerges as a Critical and Expansive Asset
TDM involves the creation, maintenance, and use of data sets. Effective TDM ensures testing is accurate, reliable, and reflective of real-world scenarios. For the broader business, TDM unlocks the under utilized data resource for broader use. It also assists in correlating data generated outside of production.
TDM unlocks the under utilized data resource for broader use. It also assists in correlating data generated outside of production. In cases where production data cannot be obtained, generating synthetic data which mimics real-world data is a viable alternative. Synthetic data generation tools, like digital twins, can create data sets that are tailored to specific testing needs. This practice allows for enhanced predictive maintenance, increased efficiency, better asset management, and improved decision making.
Just as other documents are managed through version control systems, test data should also be versioned. This practice allows teams to track changes to test data, revert to previous versions if necessary, and ensure consistency across different testing phases. Version control for test data enhances traceability and accountability. Celeris has established the iRIS tool, to facilitate the tracking of data through the test phases and to shed light on the whole testing campaign.
Conducting data coverage analysis helps identify gaps in test data and ensures all possible scenarios are tested. By analyzing the coverage of test data, teams can make informed decisions about which data sets need to be expanded or refined to achieve comprehensive testing. Regardless of the data coverage analysis employed, TDM combined with AI/ML can significantly enhance the analysis. Regularly refreshing and maintaining test data is crucial to ensure its relevance and accuracy. Outdated data can lead to inaccurate test results and missed defects. Automated data refresh processes can help keep test data dashboards up-to-date and aligned with the latest production data. To address this, Celeris has strived to connect our tools to all necessary data streams and to promote automation.
Effective TDM is a cornerstone of accurate and reliable testing. By adopting practices such as synthetic data generation, version control, coverage analysis, and refresh automation organizations can ensure their test data is valuable, organized, and efficient. Adopting these practices requires a combination of robust processes, automated tools, and commitment. By prioritizing TDM, organizations can enhance the quality of their AI/ML products and build trust with their users. At Celeris we pride ourselves on the capability to provide our customers with this kind of support.